Experience Fleet Pi, an UI first experience Pi coding agent, beyond TUI Beyond rigid frameworks. Native natural generative UI. It adapt to you, not the other way around and evolutive. No technical skills needed. A UI-first workspace for everyone. Built on Pi. 100% OSS.

Run easily your own RLM that uses by @DSPyOSS dspy.RLM and @modal Sandboxes 🔥 dspy.RLM handles the prompting Modal provide a full sandbox environnement opentui provides the TUI alternatively, fleet-rlm provide also a Claude code sub-agents /or Claude Team as well Watch a demo of it running 👇

First large-scale study of AI agents actually running in production. The hype says agents are transforming everything. The data tells a different story. Researchers surveyed 306 practitioners and conducted 20 in-depth case studies across 26 domains. What they found challenges common assumptions about how production agents are built. The reality: production agents are deliberately simple and tightly constrained. 1) Patterns & Reliability - 68% execute at most 10 steps before requiring human intervention. - 47% complete fewer than 5 steps. - 70% rely on prompting off-the-shelf models without any fine-tuning. - 74% depend primarily on human evaluation. Teams intentionally trade autonomy for reliability. Why the constraints? Reliability remains the top unsolved challenge. Practitioners can't verify agent correctness at scale. Public benchmarks rarely apply to domain-specific production tasks. 75% of interviewed teams evaluate without formal benchmarks, relying on A/B testing and direct user feedback instead. 2) Model Selection The model selection pattern surprised researchers. 17 of 20 case studies use closed-source frontier models like Claude Sonnet 4, Claude Opus 4.1, and GPT o3. Open-source adoption is rare and driven by specific constraints: high-volume workloads where inference costs become prohibitive, or regulatory requirements preventing data sharing with external providers. For most teams, runtime costs are negligible compared to the human experts the agent augments. 3) Agent Frameworks Framework adoption shows a striking divergence. 61% of survey respondents use third-party frameworks like LangChain/LangGraph. But 85% of interviewed teams with production deployments build custom implementations from scratch. The reason: core agent loops are straightforward to implement with direct API calls. Teams prefer minimal, purpose-built scaffolds over dependency bloat and abstraction layers. 4) Agent Control Flow Production architectures favor predefined static workflows over open-ended autonomy. 80% of case studies use structured control flow. Agents operate within well-scoped action spaces rather than freely exploring environments. Only one case allowed unconstrained exploration, and that system runs exclusively in sandboxed environments with rigorous CI/CD verification. 5) Agent Adoption What drives agent adoption? It's simply the productivity gains. 73% deploy agents primarily to increase efficiency and reduce time on manual tasks. Organizations tolerate agents taking minutes to respond because that still outperforms human baselines by 10x or more. 66% allow response times of minutes or longer. 6) Agent Evaluation The evaluation challenge runs deeper than expected. Agent behavior breaks traditional software testing. Three case study teams report attempting but struggling to integrate agents into existing CI/CD pipelines. The challenge: nondeterminism and the difficulty of judging outputs programmatically. Creating benchmarks from scratch took one team six months to reach roughly 100 examples. 7) Human-in-the-loop Human-in-the-loop evaluation dominates at 74%. LLM-as-a-judge follows at 52%, but every interviewed team using LLM judges also employs human verification. The pattern: LLM judges assess confidence on every response, automatically accepting high-confidence outputs while routing uncertain cases to human experts. Teams also sample 5% of production runs even when the judge expresses high confidence. In summary, production agents succeed through deliberate simplicity, not sophisticated autonomy. Teams constrain agent behavior, rely on human oversight, and prioritize controllability over capability. The gap between research prototypes and production deployments reveals where the field actually stands. Paper: arxiv.org/abs/2512.04123 Learn design patterns and how to build real-world AI agents in our academy: dair-ai.thinkific.com/

LeCun's warning reveals the hidden opportunity: As companies abandon LLMs for world models, they're creating a massive validation gap. These new architectures aren't just different - they're fundamentally harder to monitor and govern.

Beta release of #AgenticFleet based on @pyautogen & @chainlit_io Now continious ship toward the extentive features, open-source, no bullsh*t. Few surprise too soon.

@AgenticFleet is coming ! 👀

Conclusion started roughly 1 hour ago. sorry to not go though a deep dive maybe when i release the webapp tldr: - @builderio makes an excellent job transcribing the figma components (with their attributes etc) in a one click style - @lovable makes interactions, multipages, dynamic content, polish and publish. - no lockup. paid for lovable, while @builderio as first not mandatory. I will for the mapping components, and few others perks imo tho.